Crystallization profoundly influences the performance of glasses, governing their stability in nuclear waste forms, manufacturability in industrial systems, and functionality in glass-ceramics. However, the predictive models remain elusive due to the structural complexity of multicomponent glasses. Here, we establish a data-driven framework that integrates systematic experimental datasets, atomistic simulations and machine learning to predict crystallization in multicomponent alkali aluminoborosilicate glasses designed in the primary crystallization field of nepheline (NaAlSiO 4 ). Molecular dynamics models, validated against multinuclear MAS NMR spectra, reveal medium-range order and cluster motifs that are structurally similar to nepheline. From this, we derived a structural descriptor—the mean cumulative displacement (MCD)—that quantifies structural similarity between the glass and nepheline crystal. When combined with optical basicity, MCD enables robust separation of crystallizing and non-crystallizing glasses. A support vector machine (SVM) classifier trained on MCD and OB achieved ≥94% accuracy across 88 glass compositions, yielding interpretable decision boundaries that link chemistry, structure, and crystallization behavior. While the present study focuses on nepheline crystallization, the methodology provides a structure-based framework that could be extended to other crystalline phases and glass families in future work.
Bertani et al. (Wed,) studied this question.
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